A new hybrid sensor technology integrating existing weigh-in-motion axle configuration data combined with inductive signature data obtained from advanced inductive loop detectors is gaining interest due to its potential to provide detailed classification of truck body types as well as anonymous tracking of truck movements on freeways. However, selecting optimal deployment locations for the hybrid sensors has been persistently challenging because implementing new technologies state-wide can demand significant capital investment and logistics preparation. This article investigates two proposed strategies for optimally deploying this new technology on California freeways based on actual truck GPS trajectories: (i) A flow-interception approach to maximize the total amount of net origin-destination flows; and (ii) a truck re-identification approach to maximize insights into origins and destinations of sampled truck trips, as well as routes of those trips. The flow-interception model is capable of selecting locations emphasizing different body types with flow-based weight factors. The truck re-identification model investigates the best locations to identify heavy truck movement by selecting pairwise locations, and is shown to be sensitive to re-identification performance uncertainty.